Generalized Perceptual Features for Vocalization Analysis Across Multiple Species

This paper introduces the Greenwood Function Cepstral Coefficient (GFCC) and Generalized Perceptual Linear Prediction (GPLP) feature extraction models for the analysis of animal vocalizations across arbitrary species. These features are generalizations of the well-known Mel-Frequency Cepstral Coefficient (MFCC) and Perceptual Linear Prediction (PLP) approaches, tailored to take optimal advantage of available knowledge of each species
^*
auditory frequency range and/or audiogram data. Illustrative results are presented comparing use of the GFCC and GPLP features versus MFCC features over the same frequency ranges.